Factset Research Systems (FDS) Total Non-Current Liabilities (2009 - 2026)
Factset Research Systems' Total Non-Current Liabilities came in at $2.11 billion for fiscal Q4 2026 (quarter ended Aug 31, 2026), up 2.3% from $2.06 billion a year earlier but down 0.3% from the prior quarter.
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Analysis
Factset Research Systems (FDS) Total Non-Current Liabilities (2009 - 2026) Analysis & Trends
Going back to fiscal Q4 2010, Factset Research Systems' Total Non-Current Liabilities data covers 65 quarters.
- Total Non-Current Liabilities carries a five-year compound annual growth rate of 12.4% (FY2021 to FY2026).
- Going back by fiscal year, Total Non-Current Liabilities was $2.06 billion in FY2025 (-1.6%), $2.1 billion in FY2024 (-9.1%), $2.31 billion in FY2023 (-12.8%) and $2.65 billion in FY2022 (+125.4%).
- The five-year range for quarterly Total Non-Current Liabilities is $1.12 billion (fiscal Q1 2022) to $2.77 billion (fiscal Q3 2022).
- Year-over-year, Total Non-Current Liabilities increased in two of the last eight quarters, with an average decline of 1.7%.
- The fastest year-over-year change in Total Non-Current Liabilities over five years came in fiscal Q3 2022 (growth of 140.2%), and the weakest in fiscal Q3 2023 (a decline of 17.3%).
- Business Quant data shows FDS's Total Non-Current Liabilities at $2.12 billion (Q3 2026), $2.04 billion (Q2 2026) and $2 billion (Q1 2026) in the three fiscal quarters before Q4 2026.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Non-Current Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Msci | 235.55 Bn | 234.03 Bn | 739.80 Mn | 8.47 Bn |
| 2 | S&P Global | 137.22 Bn | 132.42 Bn | 2.64 Bn | 27.44 Bn |
| 3 | Moodys | 88.04 Bn | 81.11 Bn | 1.50 Bn | 10.51 Bn |
| 4 | Verisk Analytics | 26.17 Bn | 22.36 Bn | 567.00 Mn | 5.62 Bn |
| 5 | Equifax | 22.05 Bn | 21.44 Bn | 940.10 Mn | 7.19 Bn |
| 6 | TransUnion | 15.64 Bn | 12.48 Bn | - | 6.96 Bn |
| 7 | Factset Research Systems | 11.49 Bn | 10.35 Bn | 308.40 Mn | 2.11 Bn |
| 8 | Morningstar | 9.15 Bn | 7.04 Bn | 431.40 Mn | 2.89 Bn |
| 9 | Mastercard | 570.55 Bn | 530.63 Bn | - | 46.71 Bn |
| 10 | Cme | 110.48 Bn | 98.81 Bn | 359.20 Mn | 164.75 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Aug 31, 2026 | 2.11 Bn |
| May 31, 2026 | 2.12 Bn |
| Feb 28, 2026 | 2.04 Bn |
| Nov 30, 2025 | 2.00 Bn |
| Aug 31, 2025 | 2.06 Bn |
| May 31, 2025 | 2.12 Bn |
| Feb 28, 2025 | 2.13 Bn |
| Nov 30, 2024 | 2.01 Bn |
| Aug 31, 2024 | 2.10 Bn |
| May 31, 2024 | 2.11 Bn |
| Feb 29, 2024 | 2.15 Bn |
| Nov 30, 2023 | 2.21 Bn |
| Aug 31, 2023 | 2.31 Bn |
| May 31, 2023 | 2.29 Bn |
| Feb 28, 2023 | 2.36 Bn |
| Nov 30, 2022 | 2.46 Bn |
| Aug 31, 2022 | 2.65 Bn |
| May 31, 2022 | 2.77 Bn |
| Feb 28, 2022 | 1.12 Bn |
| Nov 30, 2021 | 1.12 Bn |
API Access
Factset Research Systems Total Non-Current Liabilities API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=total-non-current-liabilities&ticker=FDS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-non-current-liabilities", "ticker": "FDS", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=total-non-current-liabilities&ticker=FDS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();